Robust structural similarity index measure for images with non-Gaussian distortions

Abstract

• Introduction of a new robust structure similarity index based on p th order statistics. • A simple and low cost special case: l1-norm based robust structure similarity index. • Extension of this measure to colour images. • Demonstration of the success of new index on various types of distortions. Structural Similarity Index Measure (SSIM) has been a very successful tool in image processing and computer vision . After almost two decades, it is still the most popular method for quantifying the similarity between original and distorted images with applications in image quality assessment , image retrieval , video coding, computer vision, image encryption and data-hiding. Despite its general success, there are some types of distortions, such as when the distortion is of non-Gaussian character, where SSIM does not sustain its success. We provide a generalized version of SSIM utilizing l p -norm ( 1 ≤ p ≤ 2 ) based sample moments as opposed to the classical SSIM which uses l 2 -norm based sample moments. In particular, we study the l 1 -norm special case and our simulations on well-known image-databases show superior performance compared to SSIM and related measures.